CN112965990A - Low-voltage contact cabinet fault solution generation method and device - Google Patents
Low-voltage contact cabinet fault solution generation method and device Download PDFInfo
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- CN112965990A CN112965990A CN202110248316.2A CN202110248316A CN112965990A CN 112965990 A CN112965990 A CN 112965990A CN 202110248316 A CN202110248316 A CN 202110248316A CN 112965990 A CN112965990 A CN 112965990A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
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- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02B—BOARDS, SUBSTATIONS OR SWITCHING ARRANGEMENTS FOR THE SUPPLY OR DISTRIBUTION OF ELECTRIC POWER
- H02B1/00—Frameworks, boards, panels, desks, casings; Details of substations or switching arrangements
- H02B1/26—Casings; Parts thereof or accessories therefor
- H02B1/30—Cabinet-type casings; Parts thereof or accessories therefor
Abstract
The embodiment of the application discloses a method and a device for generating a low-voltage contact cabinet fault solution. The method comprises the following steps: acquiring current power distribution data and operation state data of the low-voltage contact cabinet; judging whether the low-voltage contact cabinet operates normally at present according to the power distribution data and the operation state data; and when the current operation of the low-voltage contact cabinet is determined to be abnormal, inputting the power distribution data and the operation state data to a fault comparison analysis model to determine fault parameters, and generating and outputting a corresponding solution according to the fault parameters. This application embodiment is through comparing the real-time distribution data of low pressure contact cabinet and running state data of gathering, when the distribution data and the running state data of current collection are unusual when examining, further with the data input of gathering to in the fault comparison analysis model that establishes in advance, compare the analysis model through the fault and confirm the fault parameter to the solution that the output corresponds has saved staff's time greatly, raises the efficiency.
Description
Technical Field
The embodiment of the application relates to the technical field of low-voltage contact cabinets, in particular to a method and a device for generating a fault solution of a low-voltage contact cabinet.
Background
The power distribution cabinet is the final-stage equipment of a power distribution system, is generally used in occasions with dispersed loads and less loops, distributes the electric energy of a certain circuit of the upper-stage power distribution equipment to nearby accords, and provides protection, monitoring and control.
The existing maintenance and management of the power distribution equipment adopts manual monitoring and operation and maintenance, for example, enterprises are provided with corresponding equipment managers and the like, but the faults cannot be found timely, and the maintenance and processing time of the faults is long due to lack of real-time equipment data information and the like.
Disclosure of Invention
The embodiment of the application provides a method and a device for generating a low-voltage contact cabinet fault solution, so that data can be analyzed in time and a corresponding solution can be provided, the operation of workers is facilitated, and the maintenance efficiency is improved.
In a first aspect, an embodiment of the present application provides a method for generating a low-voltage contact cabinet fault solution, including:
acquiring current power distribution data and operation state data of the low-voltage contact cabinet;
judging whether the low-voltage contact cabinet operates normally at present according to the power distribution data and the operation state data;
and when the current operation of the low-voltage contact cabinet is determined to be abnormal, inputting the power distribution data and the operation state data to a fault comparison analysis model to determine fault parameters, and generating and outputting a corresponding solution according to the fault parameters.
Further, according to distribution data and running state data judge whether the low pressure contact cabinet is normal at present, include:
preprocessing the power distribution data and the operation state data to form a current parameter set; the current parameter set comprises current parameter data of a plurality of different data types;
putting the current parameter set into a stored historical parameter set, wherein the historical parameter set comprises historical parameter data sets of a plurality of different data segments, the historical parameter data set of each data segment comprises historical parameter data of a plurality of different data types, the historical parameter data set of each data segment corresponds to an operation state result, and the operation state result comprises abnormity and normality;
acquiring all data type names in the current parameter data, and retrieving historical parameter data matched with the data types by taking the data type names as keywords;
and searching the historical parameter data set of the corresponding data segment from the historical parameter data matched with the data type according to the current parameter set so as to obtain the running state result corresponding to the current parameter set.
Further, when no historical parameter data set of the data segment corresponding to the current parameter set is retrieved from the matching historical parameter data,
and acquiring a historical parameter data set of the data segment closest to the current parameter set, and taking the running state result of the historical parameter data set of the data segment as the running state result corresponding to the current parameter set.
Further, obtaining the historical parameter data set of the data segment closest to the current parameter set includes:
acquiring a numerical value of each current parameter data in a current parameter set, and defining historical parameter data matched with the data type of the current parameter set as target parameter data;
and comparing the value of each current parameter data with the value of the corresponding data type in any one target parameter data respectively to determine the historical parameter data set of the data segment closest to the current parameter set.
Further, taking the operation state result of the historical parameter data set of the data segment as the operation state result corresponding to the current parameter set, includes:
recording the data segment of the nearest historical parameter data as a target data segment;
calculating the minimum difference and the maximum difference between each current parameter data in the current parameter set and the historical parameter data of the corresponding data type in the target data segment;
judging whether the number of all the minimum difference values lower than a first preset value is smaller than a first threshold value, and/or whether the number of all the maximum difference values higher than a second preset value is smaller than a second threshold value, and integrating the current parameter data and the target data segment into a new data segment when the number of the minimum difference values lower than the first preset value is smaller than the first threshold value, and/or the number of the maximum difference values higher than the second preset value is smaller than the second threshold value, wherein the running state result corresponding to the new data segment is consistent with the running state result corresponding to the target data segment; otherwise, an error is reported.
Further, when the number of the minimum difference values lower than the first preset value is larger than a first threshold value and/or the number of the maximum difference values higher than the second preset value is larger than a second threshold value, the current parameters are stored, and corresponding data segments are set according to current parameter data in the current parameters.
Further, the fault comparison analysis model is established in the following way:
acquiring a plurality of sets of historical fault data, wherein each set of historical fault data comprises historical power distribution data, historical operating state data and an operating state structure of a corresponding time point, and extracting data characteristics of each set of historical fault data;
and establishing a fault comparison analysis model according to the data characteristics.
In a second aspect, an embodiment of the present application provides a low-voltage contact cabinet fault solution generating apparatus, including:
a data acquisition module: the low-voltage contact cabinet power distribution system is used for acquiring current power distribution data and operation state data of the low-voltage contact cabinet;
a data judgment module: the low-voltage contact cabinet is used for judging whether the low-voltage contact cabinet operates normally or not according to the power distribution data and the operation state data;
a scheme output module: and the system is used for inputting the power distribution data and the operation state data to a fault comparison analysis model to determine fault parameters when the current operation of the low-voltage contact cabinet is determined to be abnormal, and generating and outputting a corresponding solution according to the fault parameters. ,
furthermore, the data judgment module comprises a preprocessing submodule, a data input submodule, a data retrieval submodule and a data matching submodule, wherein the preprocessing submodule is used for preprocessing the power distribution data and the operation state data to form a current parameter set; the current parameter set contains current parameter data of several different data types. The data input submodule is used for putting the current parameter set into a stored historical parameter set, the historical parameter set comprises historical parameter data sets of a plurality of different data segments, the historical parameter data set of each data segment comprises historical parameter data of a plurality of different data types, the historical parameter data set of each data segment corresponds to an operation state result, and the operation state result comprises abnormity and normality. And the data retrieval submodule is used for acquiring all data type names in the current parameter data and retrieving the historical parameter data matched with the data types by taking the data type names as keywords. And the data matching sub-module is used for retrieving the historical parameter data set of the corresponding data segment from the historical parameter data matched with the data type according to the current parameter set so as to obtain the running state result corresponding to the current parameter set.
Further, when the historical parameter data set of the data segment corresponding to the current parameter set is not retrieved from the matched historical parameter data, the data segment extracting module is executed: and the operation state result of the historical parameter data set of the data segment is used as the operation state result corresponding to the current parameter set.
Further, in the data segment selection module, obtaining the historical parameter data set of the data segment closest to the current parameter set is realized by the following modules:
a value acquisition submodule: the parameter setting module is used for acquiring the numerical value of each current parameter data in the current parameter set and defining historical parameter data matched with the data type of the current parameter set as target parameter data;
numerical ratio pair sub-module: and the data processing module is used for comparing the value of each current parameter data with the value of the corresponding data type in any one target parameter data respectively to determine the historical parameter data set of the data segment closest to the current parameter set.
Further, the operation state result of the historical parameter data set of the data segment is used as the operation state result corresponding to the current parameter set, and the operation state result is specifically realized through the following modules:
a difference value calculation module: the data segment used for recording the nearest historical parameter data is a target data segment; calculating the minimum difference and the maximum difference between each current parameter data in the current parameter set and the historical parameter data of the corresponding data type in the target data segment;
a difference value judging module: the data processing device is used for judging whether the number of all the minimum difference values lower than a first preset value is smaller than a first threshold value and/or whether the number of all the maximum difference values higher than a second preset value is smaller than a second threshold value, and integrating the current parameter data and the target data segment into a new data segment when the number of the minimum difference values lower than the first preset value is smaller than the first threshold value and/or the number of the maximum difference values higher than the second preset value is smaller than the second threshold value, wherein the running state result corresponding to the new data segment is consistent with the running state result corresponding to the target data segment; otherwise, an error is reported. And when the number of the minimum difference values lower than the first preset value is larger than a first threshold value and/or the number of the maximum difference values higher than a second preset value is larger than a second threshold value, storing the current parameter, and setting a corresponding data segment according to current parameter data in the current parameter.
In a third aspect, an embodiment of the present application provides a computer device, including: a memory and one or more processors;
the memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the low voltage contact cabinet fault solution generation method of the first aspect.
In a fourth aspect, embodiments of the present application provide a storage medium containing computer executable instructions which when executed by a computer processor are used to perform the low voltage contact cabinet fault solution generation method according to the first aspect.
This application embodiment is through comparing the real-time distribution data of low pressure contact cabinet and running state data to gathering, when the distribution data and the running state data of current collection are unusual when examining, further with the data input of gathering to in the trouble comparison analysis model that establishes in advance, confirm the fault parameter through the trouble comparison analysis model to the solution that the output corresponds has saved staff's detection, analysis time greatly, has improved the efficiency of breaking down to the maintenance.
Drawings
Fig. 1 is a flowchart of a method for generating a low-voltage contact cabinet fault solution according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a low-voltage communication cabinet fault solution generation device provided by an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, specific embodiments of the present application will be described in detail with reference to the accompanying drawings. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be further noted that, for the convenience of description, only some but not all of the relevant portions of the present application are shown in the drawings. Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the operations (or steps) as a sequential process, many of the operations can be performed in parallel, concurrently or simultaneously. In addition, the order of the operations may be re-arranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Example one
Fig. 1 shows a flowchart provided by an embodiment of the present application, and a method for generating a low-voltage contact cabinet fault solution provided by an embodiment of the present application may be executed by a low-voltage contact cabinet fault solution generating apparatus, which may be implemented by hardware and/or software and integrated in a computer device.
The following description will be given by taking an example of a method for the low-voltage interconnection cabinet fault solution generation device to execute the low-voltage interconnection cabinet fault solution generation. Referring to fig. 1, the method for generating the low-voltage contact cabinet fault solution includes:
s101: and acquiring current power distribution data and operation state data of the low-voltage contact cabinet.
The power distribution data and the operation state data of the low-voltage communication cabinet are collected in a real-time acquisition mode, and the purpose of real-time monitoring is achieved. The running state data is the state which is judged by the equipment such as a processor in the low-voltage contact cabinet, including the power failure of the low-voltage contact cabinet and the like, and is usually a simpler running state.
S102: and judging whether the low-voltage contact cabinet operates normally at present according to the power distribution data and the operation state data.
In step S101, if there is a simple abnormal operating condition problem, the operating condition data is obtained, and it can be determined in step S101 easily and quickly, but in step S102, the operating condition of the low-voltage interconnection cabinet is analyzed and determined by combining the power distribution data, especially the surface of the low-voltage interconnection cabinet may not represent the operating problem, but the actual operating condition and the abnormal condition need to be further identified in this step.
S103: and when the current operation of the low-voltage contact cabinet is determined to be abnormal, inputting the power distribution data and the operation state data to a fault comparison analysis model to determine fault parameters, and generating and outputting a corresponding solution according to the fault parameters.
In the present embodiment, the failure parameters include a failure type, a failure degree, a failure cause, and the like.
This application embodiment is through comparing the real-time distribution data of low pressure contact cabinet and running state data to gathering, when the distribution data and the running state data of current collection are unusual when examining, further with the data input of gathering to in the trouble comparison analysis model that establishes in advance, confirm the fault parameter through the trouble comparison analysis model to the solution that the output corresponds has saved staff's detection, analysis time greatly, has improved the efficiency of breaking down to the maintenance.
In this application embodiment, as a further improvement, judge whether low pressure contact cabinet is normal operation at present according to distribution data and running state data, include:
preprocessing the power distribution data and the operation state data to form a current parameter set; the current parameter set comprises current parameter data of a plurality of different data types; putting the current parameter set into a stored historical parameter set, wherein the historical parameter set comprises historical parameter data sets of a plurality of different data segments, the historical parameter data set of each data segment comprises historical parameter data of a plurality of different data types, the historical parameter data set of each data segment corresponds to an operation state result, and the operation state result comprises abnormity and normality; acquiring all data type names in the current parameter data, and retrieving historical parameter data matched with the data types by taking the data type names as keywords; and searching the historical parameter data set of the corresponding data segment from the historical parameter data matched with the data type according to the current parameter set so as to obtain the running state result corresponding to the current parameter set.
Since the power distribution data includes several types of data, the preprocessing process mainly arranges the different types of data and the operating state data into a data set, i.e., a current parameter set, which includes a plurality of different types of current parameter data. The historical parameter set is a historical parameter data set comprising different data segments, wherein the data segments refer to the numerical range of the data. The historical parameter data set of each different data comprises various types of historical parameter data.
In this embodiment, a keyword matching search algorithm, for example, full-field keyword matching, is adopted for searching the current parameter set in the history parameter set. For example, the history parameter set includes A, B, C history parameter data sets of three data segments, while the history parameter data set of the a data segment includes five data types, the B data includes four data types, and the C data segment includes six data types. First, according to a keyword matching algorithm, a historical parameter data set having the same keywords, which are usually the names of each data type, is screened out from the historical parameter set. In this embodiment, the history parameter data sets having the same keywords are selected from the history parameter sets, and in more detail, all the data types in the selected history parameter data sets may be the same as the data type of the current parameter set. And after the historical parameter data sets with the same keywords are screened out, matching the data segments, and retrieving the historical parameter data sets of the corresponding data segments from the historical parameter data with the matched data types, wherein for example, the data segments of the current parameter set fall into the data segments of one historical parameter data, which shows that the historical parameter data are corresponding and meet the requirements. And if the data segment of the current parameter set falls into the data segments of the plurality of historical parameter data, selecting the closest data segment from the data segments of the plurality of historical parameter data, and then obtaining the operation state result corresponding to the current parameter set.
Further, when the historical parameter data set of the data segment corresponding to the current parameter set is not retrieved from the matched historical parameter data, the historical parameter data set of the data segment closest to the current parameter set is obtained, and the operation state result of the historical parameter data set of the data segment is used as the operation state result corresponding to the current parameter set.
When the corresponding historical parameter data set is not matched, the fact that related records do not exist before is indicated, and the current parameter set cannot be directly input to obtain a corresponding result, therefore, the operation state result corresponding to the historical parameter data set of the data segment closest to the current parameter set is selected as the operation state result corresponding to the current parameter set, and a necessary program for solving a fault solution for the current parameter set is completed.
As a more preferable implementation of the embodiment of the present invention, the obtaining of the historical parameter data set of the data segment closest to the current parameter set may specifically be implemented as follows:
acquiring a numerical value of each current parameter data in a current parameter set, and defining historical parameter data matched with the data type of the current parameter set as target parameter data; and comparing the value of each current parameter data with the value of the corresponding data type in any one target parameter data respectively to determine the historical parameter data set of the data segment closest to the current parameter set.
Specifically, for example, the current parameter set has 5 types of current parameter data, that is, 5 values, a, B, C, D, and E, one of the historical parameter data matching the data type of the current parameter set includes 5 data types, that is, the target parameter data is also 5 values, a1, B1, C1, D1, and E1, and the other proximate historical parameter data includes five data types, that is, a2, B2, C2, D2, and E2. Compare the corresponding A to a1, A to a2, B to B1, B to B2, and so on, respectively. And each group of comparison forms a difference value, the sum of the difference values of five data of two groups of historical data parameters and five data in the current parameter data is calculated respectively, the sum of the two difference values is compared, and the group with the smaller difference value is used as the historical parameter data set of the data segment closest to the current parameter set.
On the other hand, taking the operation state result of the historical parameter data set of the data segment as the operation state result corresponding to the current parameter set, includes: recording the data segment of the nearest historical parameter data as a target data segment; calculating the minimum difference and the maximum difference between each current parameter data in the current parameter set and the historical parameter data of the corresponding data type in the target data segment; judging whether the number of all the minimum difference values lower than a first preset value is smaller than a first threshold value, and/or whether the number of all the maximum difference values higher than a second preset value is smaller than a second threshold value, and integrating the current parameter data and the target data segment into a new data segment when the number of the minimum difference values lower than the first preset value is smaller than the first threshold value, and/or the number of the maximum difference values higher than the second preset value is smaller than the second threshold value, wherein the running state result corresponding to the new data segment is consistent with the running state result corresponding to the target data segment; otherwise, an error is reported.
Since the current parameter data may have an upper limit and a lower limit, or the value corresponding to the data type in the target data segment has an upper limit and a lower limit, there is a minimum difference and a maximum difference. When one kind of current parameter data only has one numerical value and the corresponding historical parameter data of the same type in the target data segment also has only one numerical value, the maximum difference value is equal to the minimum difference value.
The first preset value and the second preset value are both values which are actively input by personnel and set in advance. Whether the number of the minimum difference values lower than the first preset value is smaller than a first threshold value and/or whether the number of the maximum difference values higher than the second preset value is smaller than a second threshold value is/are judged, that is, the number of the numerical values in the range of the obscure target data segment in the current parameter data is in a set range, that is, the set range is artificially set, and the numerical value deviation which can be received by the target data segment is considered. Within the range, the current parameter data and the target data segment can be integrated into a new data segment, and the operation state result corresponding to the new data segment is consistent with the operation state result corresponding to the target data segment.
For example, the collected current data is a to b, a is a lower limit, b is an upper limit, the current data in the target data segment is c, the difference obtained by the current data is c-a and c-b, and so on, the difference between the collected other data and the data of the same type in the target data segment is obtained to obtain the maximum difference and the minimum difference, the number of all the maximum differences in the whole current parameter data which are higher than the second preset value is counted, the number of the minimum differences which are lower than the first preset value is counted, if c-a is the minimum difference of the current data, the value of c-a is lower than the first preset value, the number which is lower than the first preset value is added with 1, and so on, the comparison process in the scheme can be finally completed.
And when the number of the minimum difference values lower than the first preset value is larger than a first threshold value and/or the number of the maximum difference values higher than a second preset value is larger than a second threshold value, storing the current parameters and setting corresponding data segments according to current parameter data in the current parameters. In the present embodiment, the calculation statistics are the same as described above.
In the embodiment of the present invention, the method for constructing the fault comparison analysis model is preferably as follows:
acquiring a plurality of sets of historical fault data, wherein each set of historical fault data comprises historical power distribution data, historical operating state data and an operating state structure of a corresponding time point, and extracting data characteristics of each set of historical fault data; and establishing a fault comparison analysis model according to the data characteristics.
Example two
As shown in fig. 2, an embodiment of the present application provides a low-voltage contact cabinet fault solution generating device, which includes a data obtaining module 201, a data determining module 202, and a solution outputting module 203. The data acquisition module 201 is configured to acquire current power distribution data and operation state data of the low-voltage contact cabinet; the data judgment module 202 is used for judging whether the low-voltage contact cabinet operates normally at present according to the power distribution data and the operation state data; the scheme output module 203 is configured to, when it is determined that the current operation of the low-voltage contact cabinet is abnormal, input the power distribution data and the operation state data to a fault comparison analysis model to determine a fault parameter, and generate and output a corresponding solution according to the fault parameter.
Furthermore, the data judgment module comprises a preprocessing submodule, a data input submodule, a data retrieval submodule and a data matching submodule. Specifically, the preprocessing submodule is used for preprocessing the power distribution data and the operation state data to form a current parameter set; the current parameter set contains current parameter data of several different data types. The data input submodule is used for putting the current parameter set into a stored historical parameter set, the historical parameter set comprises historical parameter data sets of a plurality of different data segments, the historical parameter data set of each data segment comprises historical parameter data of a plurality of different data types, the historical parameter data set of each data segment corresponds to an operation state result, and the operation state result comprises abnormity and normality. And the data retrieval submodule is used for acquiring all data type names in the current parameter data and retrieving the historical parameter data matched with the data types by taking the data type names as keywords. And the data matching sub-module is used for retrieving the historical parameter data set of the corresponding data segment from the historical parameter data matched with the data type according to the current parameter set so as to obtain the running state result corresponding to the current parameter set.
As a preferred embodiment, when the historical parameter data set of the data segment corresponding to the current parameter set is not retrieved from the matched historical parameter data, the data segment extracting module is executed: and the operation state result of the historical parameter data set of the data segment is used as the operation state result corresponding to the current parameter set.
In the data segment selection module, obtaining the historical parameter data set of the data segment closest to the current parameter set is realized by the following modules:
a value acquisition submodule: the parameter setting module is used for acquiring the numerical value of each current parameter data in the current parameter set and defining historical parameter data matched with the data type of the current parameter set as target parameter data;
numerical ratio pair sub-module: and the data processing module is used for comparing the value of each current parameter data with the value of the corresponding data type in any one target parameter data respectively to determine the historical parameter data set of the data segment closest to the current parameter set.
Preferably, the operation state result of the historical parameter data set of the data segment is used as the operation state result corresponding to the current parameter set, and the method is specifically implemented by the following modules:
a difference value calculation module: the data segment used for recording the nearest historical parameter data is a target data segment; calculating the minimum difference and the maximum difference between each current parameter data in the current parameter set and the historical parameter data of the corresponding data type in the target data segment;
a difference value judging module: the data processing device is used for judging whether the number of all the minimum difference values lower than a first preset value is smaller than a first threshold value and/or whether the number of all the maximum difference values higher than a second preset value is smaller than a second threshold value, and integrating the current parameter data and the target data segment into a new data segment when the number of the minimum difference values lower than the first preset value is smaller than the first threshold value and/or the number of the maximum difference values higher than the second preset value is smaller than the second threshold value, wherein the running state result corresponding to the new data segment is consistent with the running state result corresponding to the target data segment; otherwise, an error is reported. And when the number of the minimum difference values lower than the first preset value is larger than a first threshold value and/or the number of the maximum difference values higher than a second preset value is larger than a second threshold value, storing the current parameter, and setting a corresponding data segment according to current parameter data in the current parameter.
EXAMPLE III
An embodiment of the present application provides a computer device, including: a memory and one or more processors; the memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the method for generating the low-voltage contact cabinet fault solution according to the embodiment of the invention.
Example four
The embodiment of the present application further provides a storage medium containing computer executable instructions, where the computer executable instructions are executed by a computer processor to perform the method for generating a fault solution of a contact cabinet as provided in the above embodiment, and the method for generating a fault solution of a contact cabinet includes: acquiring current power distribution data and operation state data of the low-voltage contact cabinet; judging whether the low-voltage contact cabinet operates normally at present according to the power distribution data and the operation state data; and when the current operation of the low-voltage contact cabinet is determined to be abnormal, inputting the power distribution data and the operation state data to a fault comparison analysis model to determine fault parameters, and generating and outputting a corresponding solution according to the fault parameters.
Storage medium-any of various types of memory devices or storage devices. The term "storage medium" is intended to include: mounting media such as CD-ROM, floppy disk, or tape devices; computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, Lanbas (Rambus) RAM, etc.; non-volatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in a first computer system in which the program is executed, or may be located in a different second computer system connected to the first computer system through a network (such as the internet). The second computer system may provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations, such as in different computer systems that are connected by a network. The storage medium may store program instructions (e.g., embodied as a computer program) that are executable by one or more processors.
Of course, the storage medium provided in the embodiments of the present application contains computer-executable instructions, and the computer-executable instructions are not limited to the cable monitoring method based on a smart device as described above, and may also perform related operations in the cable monitoring method based on a smart device as provided in any embodiment of the present application.
The device, the apparatus, and the storage medium for generating a fault solution of a contact cabinet provided in the foregoing embodiments may execute the method for generating a fault solution of a contact cabinet provided in any embodiment of the present application, and reference may be made to the method for generating a fault solution of a contact cabinet provided in any embodiment of the present application without detailed technical details described in the foregoing embodiments.
The foregoing is considered as illustrative of the preferred embodiments of the invention and the technical principles employed. The present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present application has been described in more detail with reference to the above embodiments, the present application is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present application, and the scope of the present application is determined by the scope of the claims.
Claims (10)
1. The method for generating the fault solution of the low-voltage contact cabinet is characterized by comprising the following steps of:
acquiring current power distribution data and operation state data of the low-voltage contact cabinet;
judging whether the low-voltage contact cabinet operates normally at present according to the power distribution data and the operation state data;
and when the current operation of the low-voltage contact cabinet is determined to be abnormal, inputting the power distribution data and the operation state data to a fault comparison analysis model to determine fault parameters, and generating and outputting a corresponding solution according to the fault parameters.
2. The low-voltage communication cabinet fault solution generation method according to claim 1,
whether the low-voltage contact cabinet operates normally at present is judged according to the power distribution data and the operation state data, and the method comprises the following steps:
preprocessing the power distribution data and the operation state data to form a current parameter set; the current parameter set comprises current parameter data of a plurality of different data types;
putting the current parameter set into a stored historical parameter set, wherein the historical parameter set comprises historical parameter data sets of a plurality of different data segments, the historical parameter data set of each data segment comprises historical parameter data of a plurality of different data types, the historical parameter data set of each data segment corresponds to an operation state result, and the operation state result comprises abnormity and normality;
acquiring all data type names in the current parameter data, and retrieving historical parameter data matched with the data types by taking the data type names as keywords;
and searching the historical parameter data set of the corresponding data segment from the historical parameter data matched with the data type according to the current parameter set so as to obtain the running state result corresponding to the current parameter set.
3. The low-voltage communication cabinet fault solution generation method according to claim 2,
when no historical parameter data set of the data segment corresponding to the current parameter set is retrieved from the matching historical parameter data,
and acquiring a historical parameter data set of the data segment closest to the current parameter set, and taking the running state result of the historical parameter data set of the data segment as the running state result corresponding to the current parameter set.
4. The low-voltage communication cabinet fault solution generation method according to claim 3,
obtaining a historical parameter data set of a data segment closest to a current parameter set, comprising:
acquiring a numerical value of each current parameter data in a current parameter set, and defining historical parameter data matched with the data type of the current parameter set as target parameter data;
and comparing the value of each current parameter data with the value of the corresponding data type in any one target parameter data respectively to determine the historical parameter data set of the data segment closest to the current parameter set.
5. The low-voltage communication cabinet fault solution generation method according to claim 4,
taking the operation state result of the historical parameter data set of the data segment as the operation state result corresponding to the current parameter set, and the operation state result comprises the following steps:
recording the data segment of the nearest historical parameter data as a target data segment;
calculating the minimum difference and the maximum difference between each current parameter data in the current parameter set and the historical parameter data of the corresponding data type in the target data segment;
judging whether the number of all the minimum difference values lower than a first preset value is smaller than a first threshold value, and/or whether the number of all the maximum difference values higher than a second preset value is smaller than a second threshold value, and integrating the current parameter data and the target data segment into a new data segment when the number of the minimum difference values lower than the first preset value is smaller than the first threshold value, and/or the number of the maximum difference values higher than the second preset value is smaller than the second threshold value, wherein the running state result corresponding to the new data segment is consistent with the running state result corresponding to the target data segment; otherwise, an error is reported.
6. The method for generating the low-voltage communication cabinet fault solution according to claim 5, wherein when the number of the minimum difference values lower than the first preset value is larger than a first threshold value and/or the number of the maximum difference values higher than the second preset value is larger than a second threshold value, the current parameters are stored, and corresponding data segments are set according to current parameter data in the current parameters.
7. The method for generating the fault solution of the low-voltage contact cabinet according to claim 1, wherein the fault comparison analysis model is established by the following steps:
acquiring a plurality of sets of historical fault data, wherein each set of historical fault data comprises historical power distribution data, historical operating state data and an operating state structure of a corresponding time point, and extracting data characteristics of each set of historical fault data;
and establishing a fault comparison analysis model according to the data characteristics.
8. A low-voltage contact cabinet fault solution generation apparatus, comprising:
a data acquisition module: the low-voltage contact cabinet power distribution system is used for acquiring current power distribution data and operation state data of the low-voltage contact cabinet;
a data judgment module: the low-voltage contact cabinet is used for judging whether the low-voltage contact cabinet operates normally or not according to the power distribution data and the operation state data;
a scheme output module: and the system is used for inputting the power distribution data and the operation state data to a fault comparison analysis model to determine fault parameters when the current operation of the low-voltage contact cabinet is determined to be abnormal, and generating and outputting a corresponding solution according to the fault parameters.
9. A computer device, comprising: a memory and one or more processors;
the memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the low voltage contact cabinet fault solution generation method of any one of claims 1 to 7.
10. A storage medium containing computer executable instructions which when executed by a computer processor are operable to perform the low voltage tie cabinet fault solution generation method of any of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113285358A (en) * | 2021-06-25 | 2021-08-20 | 苏州道和电力设计安装有限公司 | Power distribution cabinet accident monitoring and alarming method and system |
| CN116052389A (en) * | 2023-01-28 | 2023-05-02 | 易电务(北京)科技有限公司 | Low-voltage capacitor cabinet fault alarm method, system and device |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113285358A (en) * | 2021-06-25 | 2021-08-20 | 苏州道和电力设计安装有限公司 | Power distribution cabinet accident monitoring and alarming method and system |
| CN116052389A (en) * | 2023-01-28 | 2023-05-02 | 易电务(北京)科技有限公司 | Low-voltage capacitor cabinet fault alarm method, system and device |
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